For most of modern biology, the hunt for new human proteins has followed a familiar path: read the genetic sequence, predict where genes begin and end, and work outward from there. That strategy has served science remarkably well, delivering the molecular blueprints behind hormones, receptors, enzymes and channels that now anchor entire fields of medicine. But a team at Sylvester Comprehensive Cancer Center, part of the University of Miami Miller School of Medicine, suspected that something important was being missed. In a new study published in Nature, the researchers describe how artificial intelligence, applied not to gene sequences but to the three-dimensional shapes of proteins, allowed them to uncover a population of hidden proteins in the human body and, for the first time, reveal what one of them actually does.
The team, led by senior author Daniel G. Isom, Ph.D., a Sylvester researcher and faculty member in the Department of Molecular and Cellular Pharmacology, turned to a vast computational dataset containing more than 214 million predicted protein structures. Rather than scanning for familiar sequence signatures, the researchers asked a different question: which of these predicted proteins fold into shapes that resemble known functional families, even if their sequences look like nothing recognizable? Their search concentrated on the G protein-coupled receptor family, or GPCRs, an enormous group of membrane proteins that allow cells to sense and respond to signals arriving from outside the cell. GPCRs are among the most heavily exploited targets in pharmacology, so any undiscovered relatives are of more than academic interest.
“For decades, we have largely explored protein biology using sequence as our guide,” Isom said. “We wanted to know what biology we might be missing if we searched by three-dimensional structure instead. What we found suggests there is another layer of biology that has been hiding in plain sight.” The structural approach flagged a set of proteins that classical sequence-based methods had never connected to the GPCR family, a category sometimes described as part of the dark proteome, the substantial fraction of predicted proteins whose functions remain entirely unknown.
One protein in particular stood out. Known as TM184C, it folded like a GPCR, but when the team examined its behavior in cells, it refused to follow the expected script. Classic GPCRs typically sit in the plasma membrane at the cell’s surface, waiting for extracellular ligands. TM184C, by contrast, was found largely inside the cell, embedded in the membranes of intracellular vesicles, the tiny membrane-bound packages that cells use to ferry materials between compartments and to one another. That difference in localization alone hinted that TM184C might represent an entirely new mode of GPCR-like function.
Following the protein’s location in living cells revealed something stranger still. The TM184C-positive vesicles did not sit still. They traveled along microtubules, the rigid protein filaments that serve as the cell’s internal highway system, and they accumulated in thin projections that extend from one cell toward its neighbors. These projections acted like bridges. Through them, the researchers observed cells exchanging metabolites, vesicles, and even entire organelles, including mitochondria, the power-generating structures that supply the energy currency of life. The discovery suggests that direct, physically connected exchange between neighboring cells may be far more common, and far more consequential, than previously appreciated.
“When we saw TM184C-positive vesicles moving through connections between cells, we realized these structures could be routes for substantial material exchange,” said Jenniffer Arcuri, Ph.D., a senior scientist with Sylvester and the study’s lead author. “That completely changed how we thought about TM184C and made us consider how cells might use these connections to cooperate and compete for resources.” To test whether the protein actually mattered, the team disrupted TM184C in cultured cells. The effect was clear: the cells formed fewer intercellular connections, and their overall shape and vesicle organization changed, indicating that TM184C helps build and manage these intercellular conduits rather than merely riding along inside them.
The findings raise a question that cuts to the heart of tissue biology: when neighboring cells share resources, who benefits? In healthy tissue, the exchange could be a form of cooperation, allowing cells under stress to survive by shuttling fuel, building blocks or damaged components to wherever they are needed most. But the conduits could also be exploited. If the exchange is unequal, one cell might gain at another’s expense, drawing support from a weaker neighbor. That possibility becomes especially provocative in cancer, where tumor cells frequently endure low oxygen and scarce nutrients. Intercellular bridges could give some cancer cells a lifeline, allowing them to share resources or siphon support from surrounding tissue in ways that conventional metabolic studies, which typically analyze cells in isolation, would never detect.
“I think cells coordinate until they have to compete,” said Shraddha Chandthakuri, a Cancer Biology doctoral student in the Isom lab. “When the cells are stressed, they may coordinate to redistribute the proteins, organelles, and metabolites to support the survival of the population as a whole.” Bruno Colon, a Molecular and Cellular Pharmacology graduate student in the same lab, is now probing that dynamic directly. “What excites me most is understanding what this exchange actually does to the cells on both sides,” Colon said. “As part of my doctoral work in the Isom lab, I am studying how these connections occur in normal cells and aggressive cancers like glioblastoma. Understanding their role could give us new insight into how these tumors communicate and potentially reveal vulnerabilities we haven’t recognized before.”
TM184C appears to influence more than just physical connectivity. The protein also seems to help regulate autophagy, the recycling program by which cells break down and reuse old or damaged components, a process critical to surviving starvation and other stresses. In the study, when the researchers reduced the amount of TM184C in cells, markers of autophagy rose, suggesting the protein normally acts as a brake or tuning mechanism on the process. Adding a structural dimension to the evidence, the team studied a yeast protein called Hfl1 that resembles the human protein. When Hfl1 was removed from yeast, the cells developed noticeable defects. Remarkably, inserting human TM184C into those yeast rescued the problems, demonstrating that the protein’s essential function has been conserved across roughly a billion years of evolution separating baker’s yeast from humans.
For Isom, the broader lesson is about method as much as mechanism. He emphasized that the work depended on pairing AI-driven structure prediction with rigorous experimental validation, not on trusting the algorithms alone. “AI cannot be blindly trusted, but can lead to really big things in the hands of experts and prepared minds,” he said. “For decades, biomedical research has understandably concentrated on the proteins we could identify and understand. But there is another layer of biology that has remained largely invisible to us. AI gives us a way to start exploring it systematically. TM184C is one example of what can be found when we look.” The implication is that artificial intelligence is not merely accelerating the pace of existing science; it is changing what scientists can see at all. TM184C, pulled out of the dark proteome by searching the shapes rather than the sequences of 214 million predicted proteins, offers both a new way to study how cells communicate, survive stress and possibly drive disease, and a template for finding whatever else has been hiding in plain sight.
Subject of Research: Discovery and functional characterization of the hidden GPCR-like human protein TM184C, which regulates intercellular exchange and autophagy
Article Title: AI helps find hidden human proteins and reveals what they do
Article References: AI helps find hidden human proteins and reveals what they do. (n.d.). Original publication
Image Credits: AI Generated
DOI: Not provided
Keywords: TM184C, GPCR, dark proteome, artificial intelligence, protein structure prediction, intercellular exchange, autophagy, vesicles, mitochondria, cell-to-cell bridges, Sylvester Comprehensive Cancer Center, cancer biology
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Drew Townsend. (September 12, 2026). AI Uncovers Hidden Human Protein That Builds Bridges Between Cells. Scienmag. https://scienmag.com/ai-uncovers-hidden-human-protein-that-builds-bridges-between-cells/
Drew Townsend. “AI Uncovers Hidden Human Protein That Builds Bridges Between Cells.” Scienmag, 12 September 2026, https://scienmag.com/ai-uncovers-hidden-human-protein-that-builds-bridges-between-cells/. Accessed 12 September 2026.
Drew Townsend. “AI Uncovers Hidden Human Protein That Builds Bridges Between Cells.” Scienmag. September 12, 2026. https://scienmag.com/ai-uncovers-hidden-human-protein-that-builds-bridges-between-cells/
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Tags: 3D protein shape analysisadvanced protein structure datasetsAI-driven biomedical researchArtificial IntelligenceArtificial intelligence in protein structure predictionautophagycancer biologycell-to-cell bridgescomputational biology in medicinedark proteomediscovery of hidden human proteinsGPCRimpact on disease understandingintercellular exchangemitochondriamolecular blueprints in biologynovel human protein functionsprotein bridging between cellsprotein folding and functionprotein structure predictionstructural bioinformaticsSylvester Comprehensive Cancer CenterTM184Cvesicles
